Do end of treatment assessments predict outcome at follow‐up in eating disorders?
Bibliographic record
Abstract
OBJECTIVE: To examine the predictive value of end of treatment (EOT) outcomes for longer term recovery status. METHOD: We used signal detection analysis to identify the best predictors of recovery based on outcome at EOT using five different eating disorder samples from randomized clinical treatment trials. We utilized a transdiagnostic definition of recovery that included normalization of weight and eating related psychopathology. RESULTS: Achieving a body weight of 95.2% of expected body weight by EOT is the best predictor of recovery for adolescents with anorexia nervosa (AN). For adults with AN, the most efficient predictor of weight recovery (BMI > 19) was weight gain to greater than 85.8% of ideal body weight. In addition, for adults with AN, the most efficient predictor of psychological recovery was achievement of an eating disorder examination (EDE) weight concerns score below 1.8. The best predictor of recovery for adults with Bulimia Nervosa (BN) was a frequency of compensatory behaviors less than two times a month. For adolescents with BN, abstinence from purging and reduction in the EDE restraint score of more than 3.4 from baseline to EOT were good predictors of recovery. For adults with binge eating disorder, reduction of the Global EDE score to within the normal range (<1.58) was the best predictor of recovery. DISCUSSION: The relationship between EOT response and recovery remains understudied. Utilizing a transdiagnostic definition of recovery, no uniform predictors were identified across all eating disorder diagnostic groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".